Assessing Approximations for Gaussian Process Classification

Kuss, M; Rasmussen, CE

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Assessing Approximations for Gaussian Process Classification

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Kuss, M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Rasmussen, CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Kuss, M., & Rasmussen, C. (2006). Assessing Approximations for Gaussian Process Classification. In Y. Weiss, B. Schölkopf, & J. Platt (Eds.), Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference (pp. 699-706). Cambridge, MA, USA: MIT Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D1DF-C

Abstract

Gaussian processes are attractive models for probabilistic classification but unfortunately exact inference is analytically intractable. We compare Laplaceamp;amp;amp;amp;amp;amp;amp;amp;lsquo;s method and Expectation Propagation (EP) focusing on marginal likelihood estimates and predictive performance. We explain theoretically and corroborate empirically that EP is superior to Laplace. We also compare to a sophisticated MCMC scheme and show that EP is surprisingly accurate.